{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### 如何使我们的图表更加漂亮和美观"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pandas_datareader import data, wb\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start = datetime.datetime(2015,1,1)\n",
    "end = datetime.datetime(2017,12,31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = data.DataReader('VIXCLS', 'fred', start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIXCLS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>755.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.534556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.260702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.740000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           VIXCLS\n",
       "count  755.000000\n",
       "mean    14.534556\n",
       "std      4.260702\n",
       "min      9.140000\n",
       "25%     11.540000\n",
       "50%     13.480000\n",
       "75%     15.980000\n",
       "max     40.740000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "style.use('seaborn-dark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Uc+bMAQDMnj0bZ86cwXPPPYe8vDzn78THxwdvxS0IoVHCccBfBibjpYMVonN1TZYMKyYT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\nNEhkCIIgiKBBIkMQBEEEjf8PHDGyKuHWlDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd36916d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['VIXCLS'].plot()\n",
    "# plt.Figure(figsize=(400,300))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
